Text prediction using multiple devices

ABSTRACT

A first set of characters may be received in response to a user input for text prediction. An estimate may be generated indicating what second set of characters will be inputted. The generating an estimate may be based on at least receiving data from a second user device. At least some of the data may not be located within the second user device&#39;s text dictionary. At least some of the data may be provided to the first user device.

BACKGROUND

This disclosure relates generally to text input systems, and morespecifically, to predicting characters based at least on locatingcharacter(s) within multiple devices.

The rise in mobile technology and communication needs have resulted inthe growth of various communication protocols/mechanisms, such as ShortMessage Service (SMS), Multimedia Messaging Service (MMS), instantmessaging, etc. As such, users may utilize keyboards (e.g., virtualkeyboards) or other user interfaces to input various characters in orderto communicate with other users or propose a query. Inputting individualtext characters can be arduous for users. One mechanism utilized toovercome this is the implementation of predictive text input. Predictivetext input predicts characters, such as a word, that a user will typebased on receiving limited input from the user. Typically, when a userinputs a few characters of a word, for example, a dictionary (e.g., aword database) stored in the same device's memory is looked up in orderto estimate what words the user will type and present them assuggestions to the user. The user may then select the suggestions toinsert into a text field. This potentially reduces the number of times auser has to input individual characters in order to complete the word.

SUMMARY

One or more embodiments are directed to a computer-implemented method, asystem, and a computer program product. A first user device may receivea first set of one or more characters in response to an input. The firstuser device may predict a second set of one or more characters that willbe inputted. The predicting may be based on at least receivinginformation that is displayed to a second user device. At least thesecond set of characters may be provided to the first user device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computing environment, according toembodiments.

FIG. 2 is a block diagram of a computing environment, according toembodiments.

FIG. 3 is a block diagram of a computing environment, according toembodiments.

FIG. 4A is a block diagram illustrating the registration of attributedata and receiving of adjusted attribute data, according to embodiments.

FIG. 4B is a table representing at least some of the attribute data ofFIG. 4A, according to embodiments.

FIG. 5 is a flow diagram of an example process for text prediction usingtwo user devices, according to embodiments.

FIG. 6 is a flow diagram of an example process for analyzing sets ofdata from different user devices, according to embodiments.

FIG. 7 depicts a cloud computing environment according to embodiments.

FIG. 8 is a block diagram of a cloud computing node, according toembodiments.

FIG. 9 depicts abstraction model layers according to embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to predicting characters basedat least on using non-local data. While the present disclosure is notnecessarily limited to such applications, various aspects of thedisclosure may be appreciated through a discussion of various examplesusing this context.

In various instances, predictive text input today may be inconvenient,error-prone, and lack a robust corpus of data for prediction. One suchinstance arises when a user desires to input a string that is notrecognized or located in any of the dictionaries in any of the user'sdevices. For example, users may input characters of a first string on afirst device, but the entire first string may only be displayed/storedto a second device owned by the same user. However, the first string maynot be located within the second device's text dictionary (e.g., becausethe first string may only be displayed on an open window of the seconddevice). In an illustrative example, a user may own both a mobile phoneand a laptop computing device. On the laptop computing device, anapplication, such as a web browser, may store or display atypicalinformation (e.g., information that is not located in a dictionary forinput) of the user, such as user-generated passwords, authenticationcodes (e.g., One-time Passwords (OTP)), Universal Unique Identifiers(UUID), serial numbers, recovery codes, payment information, phonenumbers, addresses, and/or any other private information. However, ifthe user desires to input such atypical information on his/her mobilephone (the second user device), the user may have to manually input thisinformation verbatim in order to store this information to the mobiledevice, which may be arduous. This is because neither text libraries onthe first or second devices may store this information. Recovery codes,for example, may contain 36 or more characters and so typing thesecharacters individually multiple times may become burdensome.

In some situations, a user may use another user's device (e.g., aspouse's device) to input characters on. In these situations, a user mayquery his/her device in order to obtain character(s) from his/her devicefor use in text prediction on the other user's device. Alternatively, ifthe user is utilizing his/her device, the user may query another user'sdevice in order to obtain one or more characters for text prediction.

In some embodiments, remote data stores and/or secondary user devicesmay be accessed in order to store one or more characters (e.g., letters,numbers, and/or symbols) to a user device's dictionary. These charactersmay be obtained at run time and/or at particular event/time intervals,as described in more detail below. In some embodiments, various otherremote devices may also be queried in order perform text prediction atnear real-time, as described in more detail below.

FIG. 1 is a block diagram of a computing environment 100, according toembodiments. The computing environment 100 includes the user device 102and user device 104, each of which are communicatively coupled (e.g.,via the network 108) to the server(s) 114. In some embodiments, thecomputing environment 100 may be implemented within a cloud computingenvironment, or use one or more cloud computing services, such as thecloud computing environment 50 of FIGS. 7, 8, and 9, which is describedin more detail below.

Consistent with some embodiments, the user devices 102, 104, and/or theserver(s) 114 may be configured the same as or analogous to the computersystem/server 12 as illustrated in FIG. 8. In some computingenvironments, more or fewer components may be present than illustratedin FIG. 1. For example, the user 116 may have access to an additionaluser device.

The user device 102 and/or 104 may establish a connection or communicatewith the server(s) 114 via the network 108, which may be any suitablenetwork such as, a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the internet).

The user 116 may have access to at least user device 102 and 104. Theuser devices 102 and/or 104 may be any user device, such as a mobilephone, a laptop computing device, a tablet, a smart watch, or any othersuitable device, such as those described in FIG. 8. The user device 102includes a text prediction module 106 and the text dictionary 110. Thetext prediction module 106 may be a module (e.g., program instructions)that is responsible for predicting and/or suggesting what set of (i.e.one or more) characters a user will input. For example, a graphical userinterface (GUI) may include the text prediction module 106. When theuser accesses the GUI, the user may input (e.g., type, speak) a coupleletters of a word. In response, the text prediction module 106 maypredict what word(s) the user will or is trying to input. The GUI maythen present each word as candidate words to the user for input.

The Text dictionary 110 or any other “dictionary,” “text dictionary,” or“word database” as described herein may refer to a list of one or morecharacter combinations (e.g., language words, numbers, alphanumericcombinations, and/or symbols) that a text prediction module analyzes inorder to predict and suggest strings to a particular user. Eachcharacter combination may include one or more individual/set ofcharacters (e.g., letters, numbers, symbols, etc.). Each dictionary maybe generated in various manners. For example, the text dictionary 110may be populated via a remote data store that includes each word asfound in the English language and transmits each word to the user device104. In some embodiments, the user 116 may also tailor or customizehis/her own strings, such as acronyms, slang words, etc. by enteringsuch words into the text dictionary.

FIG. 1 illustrates at least that the user device 102 may obtain thetarget string(s) 120 that are displayed to the user device 104 via theuser device targets strings data store 112. The term “target string(s)”or “target character(s)” as disclosed herein may refer to any characteror combination of characters (e.g., letters, words, numbers, symbols)that is stored to, displayed on, and/or has been historically inputtedto a particular user device that another user device requests to obtainfor text prediction. In some embodiments, target string(s) may refer toany character(s) that is not initially located in any device dictionaryof a user for text prediction. In an illustrative example, the userdevice 104 includes a display 150 that displays the target string(s)120. The user device 104, at a first time, may obtain the target string(e.g., via Object Character Recognition (OCR)), as described in moredetail below. The user device 104 may then, at a second time, transmitthe target string(s) 120 to the server(s) 114 (e.g., via the network108). The server(s) 114 may then store the target string(s) within theuser device target strings data store 112. The server(s) 114 may be anysuitable computing device(s), such as a database server, blade server,and/or any other suitable computing device, such as those described inFIG. 8.

At a third time, the user device 102 may then transmit a request to theserver(s) 114 to obtain the target string(s) 120. In response, thetarget string(s) 120 may be transmitted from the user device targetstrings data store 112 to the user device 102, via the network 108.

FIG. 2 is a block diagram of a computing environment 200, according toembodiments. FIG. 2 illustrates a particular manner in which targetstrings may be obtained by a user device. The computing environment 200includes the user device 202, the user device 204, the user device 208,and the user device 212, each of which are communicatively coupled viathe network 208.

The user device 202 includes the text prediction module 206 and the textdictionary 210. As illustrated in FIG. 2, in some embodiments, userdevices may be queried directly instead of or in addition to a queryinga remote server (e.g., the server(s) 114 of FIG. 1) to obtain targetstring(s). For example, the user device 202 may transmit a request andreceive the target string(s) 220, 230, and 240 directly from theirrespective user devices 204, 208, and 212. The network 208 may be anysuitable network, such as a Personal Area Network (PAN) (e.g., aBluetooth® network), Wi-Fi (IEEE 802.11) network, a Peer-to-Peernetwork, etc.

FIG. 3 is a block diagram of a computing environment 300, according toembodiments. The computing environment 300 includes the user device 302,which is communicatively coupled to the user device 304 via the network318. The user 316 may have access or own both of the user devices 302and 304. Each of the user devices 302 and 304 are communicativelycoupled to the remote device 314 and the user device target strings datastore 312. In some embodiments, the computing environments 100 and/or200 may be included in the computing environment 300 or vice versa.

FIG. 3 illustrates that upon a failure associated with a wirelessconnection between any one of the user device 304 or 302 and the remotedevice(s) 314 (e.g., a server computing device), a second connection maybe established with another user device. For example, at a first timethe target string(s) 310 may be transmitted from the user device 304 tothe user device target strings data store 312 via the network 308. At asecond time, the user device 302 may transmit a request to the remotedevice(s) 314 in order to retrieve the target string(s) 310 within theuser device target strings data store 312. However, a communicationfailure associated with the network 308 may occur between the userdevice 302 and the remote device(s) 314. For example, the failure may besome network latency threshold value being met, or the remote device(s)314 being inaccessible such that no session can be established, etc.When the switching module 325 (e.g., a failover module) detects thefailure (e.g., by determining that a particular time period has passedwithout establishing a connection), another request to obtain the targetstring(s) 310 may be transmitted directly to the user device 304 via thenetwork 318. In some embodiments, the request to obtain the targetstring(s) 310 may be made first to the user device 304 and if a failureoccurs between the user device 304 and user device 302, the switchingmodule 325 may route the request for the target string(s) 310 instead tothe remote device(s) 314 to obtain the target string(s) 310.

FIG. 4A is a block diagram illustrating the registration of attributedata and receiving of adjusted attribute data, according to embodiments.The attribute data 410 and 420 may include target string(s) and/ormetadata associated with particular target string(s). Accordingly,instead of or in addition to target string(s) solely being transmittedand received (e.g., the user device 102 of FIG. 1 receiving the targetstring(s) 120), the attribute data 410 may be transmitted and/orreceived. In some embodiments, aspects of FIGS. 4A and 4B may beincluded within the computing environments 100, 200, and/or 300. At afirst time, the user device 402 may accumulate and transmit theattribute data 410 to the registration server 414 via the network 408.The registration server 414 may then store the attribute data 410 withinthe data store 412, which may be the user device target strings datastore 112 or 312 of FIG. 1 or 3. At a second time, the user device 406may accumulate and transmit the attribute data 420 to store theattribute data 420 within the data store 412. At another time, the userdevice 406 may transmit a request to the data store 412 in order toreceive the adjusted attribute data 410A, which is described in moredetail below.

In some embodiments, the attribute data 410 and/or 420 is collectedactively (e.g., provided by the user device 402/application), which isdescribed in more detail below. In some embodiments, the attributes maybe collected passively (e.g., derived by a server from a client-sidescript (e.g., JavaScript)). For example, browser details that uniquelyidentify plugins and other device information may be collected fromJavaScript and queried by the registration server 414. For example, theregistration server 414 may query the user device 402 to obtain variouscharacteristics about the user device 402 via a session web browser andscripting language (e.g., JavaScript) of the client device 402. In someembodiments, the registration server 414 may send the user device 402and/or 406 a secure cookie such that session details and a client devicefingerprint are obtained.

In various embodiments, the attribute data 410 and/or 420 is transmittedto the registration server 414 in an automated fashion (e.g., as abackground task via the operating system) or transmission of the data410 and/or 420 may occur in response to a request, wherein a userprovides the attributes. This transmission of the attribute data 410and/or 420 may occur upon installation/configuration/launch of anapplication, particular events (e.g., at boot up time), and/or aparticular time intervals (e.g., every 5 minutes). In like manner, theattribute data 410 and/or 420 may be updated within the data store 412in response to the installation/configuration/launch of an application,particular events, and/or a particular time intervals. Accordingly, theuser device 402 and/or 406 may transmit attribute data at various timesto the data store 412 depending on the policy implemented.

FIG. 4B is a table 400 representing at least some of the attribute data410 and 420 of FIG. 4A, according to embodiments. The table 400 maycorrespond to one or more table data object as found within a database.The table 400 includes various fields (columns) such as, time stamp,display/cookie data, geolocation, eye tracking data, current worddatabase, word frequency and learning policies. The “device” field maycorrespond to a device ID (e.g., an International Mobile EquipmentIdentify (IMEI) identifier, Mobile Equipment Identifier (MEID)identifier, a device fingerprint, etc.). The “time stamp” field mayindicate when (e.g., a clock time/date) particular data was gathered,transmitted and/or received. The time stamp data may useful for varioustext prediction policies. For example, if the attribute data 410included display data within the last 5 minutes, then such data may beused for text prediction purposes. The “display/cookie data” field mayinclude target string(s) located on a device that is outside of a textdictionary context. For example, it may include each word as currentlydisplayed to the user device 402, and/or each word stored to a cookiefile within the user device 402.

The “geolocation” field may correspond to one or more identifiers thatindicate where the device 402 and/or 406 was located when a request wasmade to transmit/receive the attribute data 410 and/or 420. For example,the geolocation identifiers may be Global Positioning System (GPS)coordinates or indoor Beacon identifiers. Geolocation identifiers may beuseful for using words for text prediction based on geographicalcoordinates. For example, if a user was near a particular land mark,particular words associated with the landmark may be populated in the“geolocation” field and used for text prediction. Therefore, theattribute data 410 may, for example, include GPS coordinates of wherethe user device 402 was located when the transmission of the attributedata 410 occurred. The adjusted attribute data 410A may include thosewords associated with the GPS coordinates and may be transmitted to theuser device 406 in order to perform text prediction. The Adjustedattribute data 410A may be the attribute data 410 with more or fewercharacters than the attribute data 410.

The “eye tracking data” field indicates the particular positioning of auser's eyes on a display screen for predictive text purposes. Eyetracking sensors measure eye movement activity. In some embodiments, acamera eye tracking sensor on a user device directs near-infrared lighttoward the center of the eye(s), which causes reflections at the cornea.These reflections (e.g., pupil center corneal reflection (PCCR)) may betracked at various time intervals. These reflections may correspond tovalues that are populated in the “eye tracking data” field. Eye trackingdata may be useful for pruning and/or adding words to a word databasefor text prediction. As used herein, “pruning” refers to reducing thesize of decision trees or removing target string(s) from the predictivetext scoring process. Pruning may have several unforeseen advantageswithin the text prediction context. For example, pruning may reducenetwork latency, save battery, etc. Pruning may be done in variouscontexts, such as eye tracking, differences in dictionaries,active/inactive windows, etc., which is discussed in more detail below.The granularity of where the user is looking may be at various levels,such as which word(s) (and/or numbers, symbols), sentence(s),paragraph(s), and/or window(s) a particular user is looking at. In anillustrative example of pruning within the context of eye tracking, theattribute data 410 may include each word that is displayed to the userdevice 402. However, it may be determined (e.g., by the registrationserver 414) based on the eye tracking data, that the user was onlylooking at a particular sentence of the display data. Accordingly, eachword from the display may be pruned, except for the particular words ofthe sentence, which may be transmitted back to the user device 402and/or 406 as the adjusted attribute data 410A for text predictionscoring.

The “word database” field indicates a current text dictionary of a userdevice at the time of transmission/receiving of the attribute data 410.The “word frequency” field indicates how many times a particular wordand/or character combination is found on a particular user device fortext prediction purposes. The frequency that a word/charactercombination is utilized may indicate the likelihood that a user will usethat word. For example, if a word is displayed a highest quantity oftimes to user device 402, it may be likely that the word will be usedfor text prediction for user device 406. “Learning policies” may includea history of actions performed by a device for learning purposes in apredictive text context (e.g., machine learning). For example, it may bedetermined (e.g., by a predictive text module within user device 406)that every time the user type a particular word/character combination,the user inputs a particular phrase 98% of the time. This informationmay be stored within the learning policies field.

In another illustrative of example of learning policies, the user device406 may store another user device's text/image message that was sent tothe user device 406. This information may be transmitted to the datastore 412 in order to performing natural language processing (NLP),object recognition (e.g., for digital pictures), and/or machine learninganalyses for predictive text scoring. Machine learning is the process ofa computing device autonomously detecting patterns and/or associationsin data and adjusting operations (e.g., scoring, ranking, recommending,etc.) accordingly.

In an illustrative example of how components of FIGS. 4A and 4B work,the user device 402 may first transmit the attribute data 410 to thedata store 412. The attribute data 410 may include: a Device ID of theuser device 402, a time stamp of the request, various strings associatedwith the user device 402, GPS coordinates at the time of transmission,information indicating where the user was looking on the user device 402(eye tracking data), a current word database of the user device 402, atop candidate (or set of candidates) word that is displayed/inputtedmost frequently to the user device 402, and any learned policies. Theuser device 406 may then transmit a request to the data store 412 inorder to retrieve at least a portion of the attribute data 410 and/orstore some of its own data (e.g., attribute data 420) to the data store412 for text prediction. For example, the request by the user device 406may include its own time stamp information, GPS coordinates, currentword database, word frequency information, and/or its own learningpolicies.

The registration server 414 may, for example, transmit the targetstrings that are/were displayed on the user device 402 to the userdevice 406. In addition or instead, the registration server 414 maystore information associated with the GPS coordinates (e.g., the name ofa restaurant) and transmit corresponding string(s) to the user device406 for use in text prediction. The registration server 414 mayadditionally or instead analyze the eye tracking information transmittedfrom the user device 402 and responsively transmit one those strings inan area looked at by a user on a display of the user device 402. Insteadof or in addition, the registration server 414 may compare the worddatabases of each user device. The registration server 414 may thenprune those words out that are shared/identical between the two devicesand only transmit those strings to the user device 406 that the userdevice 406 does not have in its own library. Instead of or in addition,the registration server 414 may identify/determine a highest rankingcandidate word that was displayed at a highest frequency on user device402. The registration server 414 may then transmit this word to the userdevice 406 for use in text prediction scoring. In addition or insteadthe registration server may analyze learned policies from a user deviceand transmit one or more strings based on this information. For example,the user device 406 may have recently received image data of a car fromanother user. This image data may then be transmitted to theregistration server 414 such that the registration server 414 may run anobject recognition algorithm to identify what the image data is. Theidentification and/or strings associated with the image data (“car”) maythen be transmitted back to the user device 406 for text predictionscoring.

FIG. 5 is a flow diagram of an example process 500 for text predictionusing two user devices, according to embodiments. It is to be understoodthat FIG. 5 is discussed by way of illustration only. Thus, for example,more user devices may be present. The process 500 may begin at block 502when a first user device receives a target string from a second userdevice. In some instances, the target string may be displayed to thesecond user device but not be located in the second user device's textdictionary. For example, the string may be displayed to particularwindows corresponding to particular web pages. In particularembodiments, Object Character Recognition (OCR) algorithms may utilizedon the characters displayed to the second user device and thentransmitted back to the first user device for use in text prediction.For example, a set of text such as a password or other authenticationstring (e.g., a OTP) may be displayed at the second user device and auser may need to re-enter the password on the first user device. Theoperating system or an application (e.g., a web browser) may then runOCR algorithms. For example, first an image/snapshot, which includes thepassword, may be taken of the screen. The image may then be scanned andone or more characters of the password may be identified (e.g., viapattern recognition and/or feature extraction algorithms). The one ormore characters may then be converted to a second set of charactersbased on the identifying (e.g., converting the characters to AmericanStandard Code (ASC) II). The second set of characters may then beassociated with the password and then transmitted back to the first userdevice such that a text dictionary within the first user device isupdated for use in text prediction. In some embodiments, objectrecognition or other recognition algorithms may be used for digitalimages or icons for use in text prediction. For example, the first orsecond user device may intelligently scan recent text messages andrecognize one or more EMOJI characters (e.g., a smiley face icon) foruse in text prediction.

In some embodiments, other mechanisms may be utilized instead of or inaddition to OCR or object recognition methods to obtain characters fromthe second user device. For example, a module within an operating systemkernel may obtain text from the second user device. In anotherembodiment, a particular application itself may gather the text. In anillustrative example, an application of the first user device mayrequest words or context information about a particular application thatis running on the second user device. The particular application'sApplication Programming Interface (API) may then receive and route thisrequest to the particular application. The particular application maygive the API information about what the application concerns, particularwords associated with the application, etc. The API may then route thisinformation back to the first user device.

In some instances, the target string may not necessarily be displayedbut stored in various locations of the second user device, such as acookie file. The cookie file may store important information notcontained in a text dictionary, such as user-generated passwordscorresponding to authentication and authorization for functions ofparticular websites. Accordingly, in some embodiments, the first userdevice may obtain this cookie data from the second user device. In someembodiments, a history of characters that a user has typed may be logged(e.g., via key logging modules) and stored to a particular file withinthe second user device such that the information contained in theparticular filed can be queried and obtained from the first user deviceand used for text prediction.

In some embodiments, block 502 may occur at various points in time andthrough automated or user requested means. For example, at boot-up timeor particular time intervals, the first user device may be configured toquery the second user device (e.g., via the network 208 of FIG. 2) andas a background task (e.g., automated without user intervention) at thefirst user device. Accordingly, the first user device's text dictionarymay be updated periodically with characters associated with the seconduser device. In some embodiments, block 502 may occur at run-time. Forexample, in response to initiating access (e.g., opening, installing,and/or inputting character(s)) to a text-entry application (e.g., a SMStext message application) within the first user device, an automatedwireless connection (e.g., via the network 208) may be establishedbetween the first user device and the second user device. In response tothe establishing of this connection, a target string may be receivedfrom the second user device such that the target string may then be usedfor text prediction by the first user device. In run-time embodiments,block 502 (and 504) may occur, for example, after block 506.

In some embodiments, the first user device may not receive the targetstring directly from the second user device as illustrated in block 502.Rather, the target string may be retrieved from a central data store(e.g., the user device target strings data store 112 of FIG. 1). And asdiscussed above, the receiving of the target string(s) from the datastore may occur by automated, run-time, and/or user requested means atany particular time interval or boot up time.

In some embodiments, block 502 may be the result of the first userdevice requesting any string(s) associated with each of the other userdevices that are nearby to the first user device, and the second userdevice may be the device that is near to the first user device. Inembodiments, Bluetooth®, location services (Location-Based Services(LBS)), manual user registration or requests, and/or eye-trackingtechnology may be utilized to determine a device nearby to the firstuser device. For example, devices within a Bluetooth® signal strengththreshold of the first user device may be queried. In another example, auser may register each of his/her devices (e.g., via the registrationserver 414 of FIG. 4) before a particular application may be accessed.Accordingly, when a user utilizes a text prediction application, a datastore (e.g., the data store 412) may be queried to determine whatdevices a particular user owns in order to transmit a message to thosedevices to obtain data from them.

Per block 504, and in some embodiments, metadata associated with thetarget string may be received. The metadata may be any suitable type ofmetadata, such as one or more attributes as specified by the table 400of FIG. 4. For example, a timestamp identifier corresponding to when thetarget string was received may be obtained. A geolocation identifiercorresponding to where the second user device was located when thesecond user device transmitted the first target string may be obtained,etc.

Per block 506, a first set of characters may be received in response toan input. For example, a user of the first user device may activate atexting application and type two letters of a word within a GUI field ofthat application. Alternatively, the user may input a voice commandsegment of a word.

Per block 508, a second set of characters that will be inputted may bepredicted (i.e., generating a character estimate) based at least on thetarget string. The prediction at block 508 may be performed byimplementing and/or combining various text prediction algorithms. Forexample, a text prediction module (e.g., the text prediction module 106)located within the first user device may use natural language processing(NLP) mechanisms that use syntactic, semantic, and/or part of speechcontext of the first set of characters and tagging to predict the secondset of characters. The text prediction module may instead of or inaddition use frequency-based algorithms based on statistical data. Forexample, given a first set of words “I am . . . ” there may be astatistically significant chance that the next word will be “going,” andso this word would be used for prediction scoring. In another example,the top 10 words given a set of input characters (the first set ofcharacters) a user has used in the past may be used as candidatecharacters. Further, out of a large corpus of data, the top 20% of wordsused by a plurality of individuals (e.g. within a social media group)may be selected given the first set of character input.

In some embodiments, the prediction at block 508 may include scoring ortaking into account the target string(s) and/or metadata gathered fromthe second user device (and/or first user device) at block 502 and 504respectively. For example, the prediction module may take into accountvarious contextual data associated with the target string (e.g., thedata within the table 400 of FIG. 4). In an example illustration, eyetracking metadata may specify that the user was looking at particularwords within the second user device. Accordingly, those particular wordsmay be transmitted and/or scored higher as candidate words to present tothe user. In another example, it may be determined what applicationwindow on the second user device is active (e.g., the window that iscurrently being displayed). Predictive text scoring may include a rulethat character(s) on an active window are more likely to be typed by auser. Accordingly, words from inactive windows, although gathered, maybe pruned. This may include, for example, receiving (e.g., by theserver(s) 114) information from a user device that helps identify that afirst window of a plurality of windows is an active window currentlydisplayed. The first window may be associated or mapped to a first setof characters that are displayed to the first window. A second set ofcharacters displayed to a second set of windows may then be pruned suchthat the second set of characters are not used for text prediction.Accordingly, the first set of characters may be transmitted to the firstuser device.

Per block 510, the second set of characters may be provided to the firstuser device as candidate characters. For example, a passwordcorresponding to authentication of a particular user may have only beenentered and stored to the second user device. The passwords may havenever been entered on the first user device. Within in a GUI, of thefirst user device, the user may type the first two characters of thepassword. In response to the predicting at block 508, the providing ofthe second set of characters as candidates may include displaying andpresenting to user the rest of the password as a top ranked candidatefor text prediction based on the first set of characters alreadyinputted. Embodiments of the present disclosure, as illustrated above,may improve predictive text technologies, specifically by giving morerobust candidate and/or text prediction scoring according to blocks 508and/or 510.

FIG. 6 is a flow diagram of an example process 600 for analyzing sets ofdata from different user devices, according to embodiments. In someembodiments, the process 600 represents remote storage and analyzing bya non-user device entity. For example, the process 600 may represent analgorithm that the server(s) 114 and user device target string(s) datastore 112 perform in order to store and transmit data according to auser device request.

The process 600 may begin at block 602 when a first set of data (e.g., atarget string) and metadata from a first user device is received (e.g.,by the user device target string(s) data store 112). Per block 604, at asecond time, a second set of data and metadata may be received from asecond user device. For example, the table 400 that is populated mayinclude various units of information concerning the user device 402 anduser device 406.

Per block 606, a request from the second user device may be received toretrieve the data and/or metadata of the first user device for use intext prediction processes. For example, the receiving of the targetstring at block 502 in FIG. 5 may be in response to transmitting arequest for the target string.

Per block 608, the data and/or metadata from the first and/or seconduser devices may be compared and/or analyzed. For example, referringback to FIG. 4B, the word databases of both the user device 402 (thefirst user device) and the user device 406 (the second user device) maybe compared to determine whether the first user device includes wordsthat are not within the second user device's word database, for pruningpurposes as described above. In another example, eye tracking data maybe analyzed concerning the user device 402, for pruning purposes, asdescribed above.

Per block 610, it may be determined whether the data and/or metadata ofthe first user device needs adjusting for purposes of transmitting datato the second user device (or implementing text prediction). Adjustingmay include adding and/or subtracting the target strings and/or metadatareceived from the first and/or second user device for transmissionpurposes (e.g., the adjusted attribute data 410A of FIG. 4). Forexample, using the illustration at block 608 above, if it is determinedthat the first user device includes a first set of words that are notwithin the second user device's word database, and a second set of wordsthat are within the second user device's word database, the second setof words may be pruned as possible candidate words for text prediction.Accordingly, only the first set of words may be transmitted to thesecond user device for candidates using text prediction. Thus, block 614may occur such that the adjusted data may be transmitted to the seconduser device. In another example using the illustration at block 608,each word on the first device's display screen may be transmitted to thedata store 412 of FIG. 4. However, after analyzing the eye trackingdata, suggesting that the user was only looking at a portion of thedisplay screen, the words that the user was not looking at according tothe eye tracking data may be pruned for text prediction purposes.

Per block 612, if the data and/or metadata of the first user device doesnot need adjusting, the data and/or metadata of the first user devicethat was originally received may be transmitted to the second userdevice. In some embodiments, the process 600 may include performing textprediction (e.g., block 508 of FIG. 5), such that only a set ofcandidate characters will be transmitted and presented to the user aftertext prediction.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 7 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a schematic of an example of a cloud computingnode (or user computing device, such as user computing device 102) isshown. Cloud computing node 10 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. Regardless, cloud computing node 10 is capable of beingimplemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 8, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device, which is notto be construed necessarily by one of ordinary skill in the art as ageneric computer that performs generic functions. The components ofcomputer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and text prediction 96.

Aspects of the present invention may be a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the various embodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofembodiments of the present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of embodiments of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer-implemented method comprising: receiving, by a first userdevice, a first set of one or more characters in response to an input;displaying by a second user device a first image, the first imageincluding a first set of text; receiving, by the first user device, thefirst image; scanning the first image and identifying one or morecharacters of the first set of text; updating a first text dictionarywithin the first user device; predicting, by the first user device, asecond set of one or more characters that will be inputted, thepredicting being based on at least receiving information that isdisplayed to the second user device; and providing at least the secondset of characters to the first user device.
 2. The method of claim 1,further comprising receiving, over a network and by the first userdevice, a first target string from a data store, the first target stringbeing transmitted from the second user device to the data store, whereinthe predicting includes analyzing the first target string.
 3. The methodof claim 2, further comprising receiving, by the first user device,metadata associated with the first target string, the predicting beingfurther based on the metadata, wherein the metadata includes at leastone attribute of a group of attributes consisting of: a timestampidentifier corresponding to when the first target string wastransmitted, a geolocation identifier corresponding to where the seconduser device was located when the second user device transmitted thefirst target string, eye tracking data associated with the second userdevice, and a word most frequently displayed to the second user device.4. (canceled)
 5. The method of claim 1, further comprising:establishing, in response to initiating access to a text-entryapplication within the first user device, a wireless connection betweenthe first user device and the second user device; and receiving, inresponse to the establishing, a target string from the second userdevice, wherein the predicting includes analyzing the target string. 6.The method of claim 1, further comprising: receiving a plurality ofwords displayed on the second user device; receiving eye tracking datacorresponding to what particular words of the plurality of words a userwas looking at on the second user device, wherein the predicting isfurther based on the particular words the user was looking at on thesecond user device.
 7. The method of claim 1, further comprising:transmitting, over a network and by the first user device, a first textdictionary to a data store, wherein the second user device transmits asecond text dictionary to the data store, and wherein it is determinedthat the second text dictionary includes one or more words that thefirst text dictionary does not include; and receiving, in response tothe determining that the second text dictionary includes one or morewords that the first text dictionary does not include, the one or morewords from the second text dictionary.
 8. A system comprising: acomputing device having a processor; and a computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by the processor to cause the system to performa method, the method comprising: obtaining a first set of data andmetadata from a first user device; obtaining a second set of data andmetadata from a second user device; receiving, from the second userdevice, a request to retrieve the first set of data and metadata of thefirst user device for text prediction; comparing the first set of dataand metadata of the first user device with the second set of data andmetadata of the second user device; in response to the comparing,adjusting at least some of the first set of data or metadata of thefirst user device; receiving a plurality of words displayed on the firstuser device; receiving eye tracking data corresponding to whatparticular words of the plurality of words a user was looking at on thefirst user device; pruning a set of words of the plurality of words thatthe user was not looking at; transmitting, to the second user device,the adjusted first set of data or metadata of the first user device, theadjusted first set of data or metadata for use in the text predictionwithin the second user device; and wherein in response to the receivingof the request, the particular words that the user was looking at aretransmitted to the second user device and the set of words that the userwas not looking at are not transmitted, and wherein the adjustingincludes the pruning.
 9. The system of claim 8, wherein the metadata ofthe first user device includes at least one attribute of a group ofattributes consisting of: a timestamp identifier corresponding to whenthe first set of data was transmitted, a geolocation identifiercorresponding to where the first user device was located when the firstuser device transmitted the first set of data, eye tracking dataassociated with the second user device, and a word most frequentlydisplayed to the first user device.
 10. The system of claim 8, whereinthe first user device displays a first image, the first image includinga first set of text, the method further comprising: receiving, by thesecond user device, the first image; scanning the first image andidentifying one or more characters of the first set of text; andupdating, prior to the text prediction and response to the scanning, afirst text dictionary within the second user device.
 11. (canceled) 12.The system of claim 8, the method further comprising: receiving a firsttext dictionary of the first user device; receiving a second textdictionary of the second user device, the first text dictionary and thesecond text dictionary for use in text prediction; determining that thefirst text dictionary includes one or more words that the second textdictionary does not include; and transmitting, in response to thedetermining, the one or more words from the first text dictionary to thesecond user device.
 13. The system of claim 8, wherein the first set ofdata includes a plurality of words displayed to a plurality of windows,the method further comprising: identifying that a first window of theplurality of windows is an active window that is currently displayed;associating the first window with a first set of characters that aredisplayed to the first window; pruning a second set of charactersassociated with a second set of windows, wherein the second set ofcharacters are not used for the text prediction; transmitting, inresponse to the pruning, the first set of characters to the second userdevice for the text prediction.
 14. A computer program productcomprising a computer readable storage medium having program codeembodied therewith, the program code executable by a computing device toperform a method, the method comprising: receiving, by a first userdevice, a first set of characters in response to a user input for textprediction; displaying by a second user device a first image, the firstimage including a first set of text; receiving, by the first userdevice, the first image; scanning the first image and identifying one ormore characters of the first set of text; updating a first textdictionary within the first user device; generating an estimateindicating what second set of characters will be inputted, thegenerating an estimate being based on at least receiving data from asecond user device, at least some of the data not being located withinthe second user device's text dictionary; and providing at least some ofthe data to the first user device.
 15. The computer program product ofclaim 14, wherein the method further comprises receiving, over a networkand by the first user device, a first target string from a data store,the first target string being transmitted from the second user device tothe data store, wherein the generating an estimate includes analyzingthe first target string.
 16. The computer program product of claim 14,the method further comprising receiving, by the first user device,metadata associated with the data, the generating an estimate beingfurther based on the metadata, wherein the metadata includes at leastone attribute of a group of attributes consisting of: a timestampidentifier corresponding to when the data was transmitted, a geolocationidentifier corresponding to where the second user device was locatedwhen the second user device transmitted the data, eye tracking dataassociated with the second user device, and a word most frequentlydisplayed to the second user device.
 17. (canceled)
 18. The computerprogram product of claim 14, the method further comprising:establishing, in response to initiating access to a text-entryapplication within the first user device, a wireless connection betweenthe first user device and the second user device; and receiving, inresponse to the establishing, a target string from the second userdevice, wherein the generating of the estimate includes analyzing thetarget string.
 19. The computer program product of claim 14, the methodfurther comprising: receiving a plurality of words displayed on thesecond user device; receiving eye tracking data corresponding to whatparticular words of the plurality of words a user was looking at on thesecond user device, wherein the generating an estimate is further basedon the particular words the user was looking at on the second userdevice.
 20. The computer program product of claim 14, the method furthercomprising: transmitting, over a network and by the first user device, afirst text dictionary to a data store, wherein the second user devicetransmits a second text dictionary to the data store, and wherein it isdetermined that the second text dictionary includes one or more wordsthat the first text dictionary does not include; and receiving, inresponse to the determining that the second text dictionary includes oneor more words that the first text dictionary does not include, the oneor more words from the second text dictionary.